In the short term, the self-reinforcing bullish narrative for the AI sector in capital markets, driven by computing power inflation, is unlikely to reverse. However, the current state of the AI industry remains significantly distant from claims of having achieved a "fully closed commercial loop." The current perfect industry narrative is the result of multiple factors working in tandem, and the sector still needs to discover more high-value monetization scenarios to justify the massive capital expenditures (CAPEX) being deployed.
The rise in U.S. long-term bond yields and the extremely narrow margin for error at both the market and industry levels suggest the market is likely to remain highly volatile in the near term. Close attention to the risk of a temporary mismatch between the pace of AI investment and output is essential. Tracking a series of high-frequency indicators, such as token prices and corporate bond CDS spreads, serves as a necessary interim measure.
Event Context
On June 5th, U.S. technology stocks experienced a significant sell-off, with major indices recording their worst single-day performance since the so-called "Liberation Day" of the trade war last year. The S&P 500, Nasdaq Composite, and Philadelphia Semiconductor Index (SOX) fell by 2.6%, 4.2%, and 10.3% respectively during the session. We assess that the primary factors behind this sharp short-term correction were an adjustment in monetary policy expectations triggered by robust May non-farm payroll data, combined with previously crowded market positioning, and noise from events such as the impending SpaceX IPO, Broadcom's earnings report, and Google's equity financing. The subsequent trajectory for U.S. tech stocks, the probability of an AI bubble bursting, and how the AI narrative, macro-inflationary pressures, and potential liquidity squeezes from high-profile IPOs might affect the market are all critical questions requiring focused consideration.
Historical Review
Changes in macroeconomic expectations have significantly impacted the short-term performance of U.S. semiconductor hardware stocks. Our review reveals that since 2024, the Philadelphia Semiconductor Index (SOX) has experienced four notable pullbacks, each of relatively short duration (ranging from half a month to two months). These occurred in April 2024 (-15.2%), July 2024 (-31.1%), February 2025 (-49.1%), and March 2026 (-18.6%). Shifts in macroeconomic expectations were the primary trigger each time, with corresponding market concerns focusing sequentially on economic stagflation, recession, recession again, and stagflation once more.
Regarding the logical transmission between macro factors and U.S. semiconductor hardware, we judge one explanation to be particularly plausible: Since 2023, AI computing power has been the core driver of U.S. semiconductor hardware stock performance. Concurrently, U.S. tech giants have been the primary source of funding for massive AI computing investments (accounting for over 50% of the total). As AI remains in its early developmental stage, these tech giants primarily rely on their traditional businesses (internet, software, etc.) to provide continuous funding for AI investments. Simply put, the prosperity of AI computing power (and thus U.S. semiconductor hardware) depends both on the technological and application progress of the AI industry itself and on a stable and favorable macroeconomic environment as a foundation. Recent strong employment data and the tense situation in the Middle East conflict are also significantly influencing market expectations for Federal Reserve monetary policy, which in turn feeds into concerns about the sustainability of AI CAPEX.
AI Progress
The short-term industry narrative appears nearly perfect, yet it remains significantly distant from claims of having achieved a "fully closed commercial loop." Since early 2026, aided by the rapid penetration of AI Agents, the exponential growth of Anthropic's Annual Recurring Revenue (ARR), and persistently tight supply-demand dynamics for AI computing power, the AI industry has presented a picture of flourishing growth. However, we also note that the current high activity in the AI sector is supported by a confluence of favorable factors: short-term experimentation by businesses and individuals, exponential growth in AI token consumption driven by the shift from chatbots to AI Agents, price increases and a business model shift towards token-consumption-based billing due to tight computing power supply and demand.
In the short term, from upstream (semiconductor hardware) to downstream (cloud providers, model providers, etc.), the entire industry chain is significantly benefiting from inflation driven by tight computing power supply and demand. The supernormal profits created in some segments (e.g., memory chips) are difficult to explain rationally using fundamental economic principles. From a medium-term perspective, beyond existing scenarios like AI coding, we still need to discover more high-value monetization scenarios to match the massive upstream investments in AI computing power. Furthermore, from an economic standpoint, a billing model based on token consumption is more of a transitional arrangement. Ultimately, pricing should reasonably correlate with actual commercial output and usage value.
Forward Outlook
Continued high volatility is expected, with close attention required on the risk of a temporary mismatch between investment and output rhythms. From the 2000 dot-com bubble to the present, over the past two decades, the rise and fall of global technological waves have been primarily driven by industry trends, but macroeconomic factors have also played a significant influencing role.
In the short term, we judge the market is likely to remain in a state of high volatility for several reasons. Firstly, persistently high U.S. long-term bond yields are expected to keep the U.S. stock market itself highly unstable and significantly suppress market risk appetite. Secondly, benefiting from short-term tightness in computing power supply and demand, the micro-level fundamentals of the AI industry appear nearly flawless, with the industry logic continuously reinforcing itself positively. However, the current ultra-high gross margins of U.S. semiconductor and hardware companies depend on global AI CAPEX investments not decelerating. Our calculations show that the projected 2026 CAPEX for the top four North American cloud providers ($710 billion) is roughly equal to their operating cash flow for the same period. They are raising additional funds through debt issuance and equity offerings, actions that will make Wall Street more short-sighted and demanding. From a top-down perspective, the AI industry has an extremely narrow margin for error over the coming quarters.
The market harbors little doubt about the long-term trend and commercial value of the AI industry. However, considering the currently extremely crowded market positioning and the financial pressure on tech giants from massive AI CAPEX, the risk of a temporary correction due to a short-term mismatch between investment and output rhythms remains a crucial risk to monitor closely. Data points like token prices and the credit default swap (CDS) spreads of tech giants are among the preferred short-term indicators.
Key Risk Factors
Risks include persistent and potentially runaway inflation; AI technological progress falling short of expectations; risks of uncontrolled AI development; risks of contraction or slowdown in capital expenditure by tech giants; risks of global supply chain disruption and blockage due to geopolitical conflicts; and risks of policy expectation disorder ahead of the U.S. midterm elections.
Investment Strategy
The recent correction in U.S. technology stocks stems more from a combination of factors: monetary policy expectation adjustments, previously crowded market positioning, and noise from individual corporate events. In the short term, the self-reinforcing bullish logic for the AI industry is difficult to reverse, but the industry's current progress remains significantly distant from having a fully closed commercial loop, necessitating the discovery of more high-value monetization scenarios. Simultaneously, rising long-term bond yields and the extremely narrow margin for error at the market level imply the market is likely to continue exhibiting high volatility. It is imperative to closely monitor the risk of a temporary mismatch between the pace of AI investment and output. Tracking a series of high-frequency indicators serves as a necessary interim strategy for the current environment.
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